Reversible Oxidation of a Conserved Methionine in the Nuclear Export Sequence Determines Subcellular Distribution and Activity of the Fungal Nitrate Regulator NirA

The assimilation of nitrate, a most important soil nitrogen source, is tightly regulated in microorganisms and plants. In Aspergillus nidulans, during the transcriptional activation process of nitrate assimilatory genes, the interaction between the pathway-specific transcription factor NirA and the exportin KapK/CRM1 is disrupted, and this leads to rapid nuclear accumulation and transcriptional activity of NirA. In this work by mass spectrometry, we found that in the absence of nitrate, when NirA is inactive and predominantly cytosolic, methionine 169 in the nuclear export sequence (NES) is oxidized to methionine sulfoxide (Metox169). This oxidation depends on FmoB, a flavin-containing monooxygenase which in vitro uses methionine and cysteine, but not glutathione, as oxidation substrates. The function of FmoB cannot be replaced by alternative Fmo proteins present in A. nidulans. Exposure of A. nidulans cells to nitrate led to rapid reduction of NirA-Metox169 to Met169; this reduction being independent from thioredoxin and classical methionine sulfoxide reductases. Replacement of Met169 by isoleucine, a sterically similar but not oxidizable residue, led to partial loss of NirA activity and insensitivity to FmoB-mediated nuclear export. In contrast, replacement of Met169 by alanine transformed the protein into a permanently nuclear and active transcription factor. Co-immunoprecipitation analysis of NirA-KapK interactions and subcellular localization studies of NirA mutants lacking different parts of the protein provided evidence that Met169 oxidation leads to a change in NirA conformation. Based on these results we propose that in the presence of nitrate the activation domain is exposed, but the NES is masked by a central portion of the protein (termed nitrate responsive domain, NiRD), thus restricting active NirA molecules to the nucleus. In the absence of nitrate, Met169 in the NES is oxidized by an FmoB-dependent process leading to loss of protection by the NiRD, NES exposure, and relocation of the inactive NirA to the cytosol

Stochasticity from function -- why the Bayesian brain may need no noise

An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing. Since the precise statistical properties of neural activity are important in this context, many models assume an ad-hoc source of well-behaved, explicit noise, either on the input or on the output side of single neuron dynamics, most often assuming an independent Poisson process in either case. However, these assumptions are somewhat problematic: neighboring neurons tend to share receptive fields, rendering both their input and their output correlated; at the same time, neurons are known to behave largely deterministically, as a function of their membrane potential and conductance. We suggest that spiking neural networks may, in fact, have no need for noise to perform sampling-based Bayesian inference. We study analytically the effect of auto- and cross-correlations in functionally Bayesian spiking networks and demonstrate how their effect translates to synaptic interaction strengths, rendering them controllable through synaptic plasticity. This allows even small ensembles of interconnected deterministic spiking networks to simultaneously and co-dependently shape their output activity through learning, enabling them to perform complex Bayesian computation without any need for noise, which we demonstrate in silico, both in classical simulation and in neuromorphic emulation. These results close a gap between the abstract models and the biology of functionally Bayesian spiking networks, effectively reducing the architectural constraints imposed on physical neural substrates required to perform probabilistic computing, be they biological or artificial

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about journal publication. Frontiers in Neuromorphic Engineering (2019

Accelerated physical emulation of Bayesian inference in spiking neural networks

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.0120

Fast and deep: energy-efficient neuromorphic learning with first-spike times

For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems also strive for short time-to-solution and low energy-to-solution characteristics. At the level of neuronal implementation, this implies achieving the desired results with as few and as early spikes as possible. In the time-to-first-spike-coding framework, both of these goals are inherently emerging features of learning. Here, we describe a rigorous derivation of learning such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how it can implement error backpropagation in hierarchical spiking networks. Furthermore, we emulate our framework on the BrainScaleS-2 neuromorphic system and demonstrate its capability of harnessing the chip's speed and energy characteristics. Finally, we examine how our approach generalizes to other neuromorphic platforms by studying how its performance is affected by typical distortive effects induced by neuromorphic substrates.Comment: 20 pages, 8 figure

CXCL9-derived peptides differentially inhibit neutrophil migration in vivo through interference with glycosaminoglycan interactions

Several acute and chronic inflammatory diseases are driven by accumulation of activated leukocytes due to enhanced chemokine expression. In addition to specific G protein-coupled receptor-dependent signaling, chemokine–glycosaminoglycan (GAG) interactions are important for chemokine activity in vivo. Therefore, the GAG–chemokine interaction has been explored as target for inhibition of chemokine activity. It was demonstrated that CXCL9(74-103) binds with high affinity to GAGs, competed with active chemokines for GAG binding and thereby inhibited CXCL8- and monosodium urate (MSU) crystal-induced neutrophil migration to joints. To evaluate the affinity and specificity of the COOH-terminal part of CXCL9 toward different GAGs in detail, we chemically synthesized several COOH-terminal CXCL9 peptides including the shorter CXCL9(74-93). Compared to CXCL9(74-103), CXCL9(74-93) showed equally high affinity for heparin and heparan sulfate (HS), but lower affinity for binding to chondroitin sulfate (CS) and cellular GAGs. Correspondingly, both peptides competed with equal efficiency for CXCL8 binding to heparin and HS but not to cellular GAGs. In addition, differences in anti-inflammatory activity between both peptides were detected in vivo. CXCL8-induced neutrophil migration to the peritoneal cavity and to the knee joint were inhibited with similar potency by intravenous or intraperitoneal injection of CXCL9(74-103) or CXCL9(74-93), but not by CXCL9(86-103). In contrast, neutrophil extravasation in the MSU crystal-induced gout model, in which multiple chemoattractants are induced, was not affected by CXCL9(74-93). This could be explained by (1) the lower affinity of CXCL9(74-93) for CS, the most abundant GAG in joints, and (2) by reduced competition with GAG binding of CXCL1, the most abundant ELR+ CXC chemokine in this gout model. Mechanistically we showed by intravital microscopy that fluorescent CXCL9(74-103) coats the vessel wall in vivo and that CXCL9(74-103) inhibits CXCL8-induced adhesion of neutrophils to the vessel wall in the murine cremaster muscle model. Thus, both affinity and specificity of chemokines and the peptides for different GAGs and the presence of specific GAGs in different tissues will determine whether competition can occur. In summary, both CXCL9 peptides inhibited neutrophil migration in vivo through interference with GAG interactions in several animal models. Shortening CXCL9(74-103) from the COOH-terminus limited its GAG-binding spectrum

Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate

We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of 1000 compared to biological dynamics enables the execution of computationally expensive tasks, by allowing the fast emulation of long-duration experiments or rapid iteration over many consecutive trials. The flexibility of our architecture is demonstrated in a suite of five distinct experiments, which emphasize different aspects of the BrainScaleS-2 system

The effect of the decoy molecule PA401 on CXCL8 levels in bronchoalveolar lavage fluid of patients with cystic fibrosis.

BACKGROUND: The chemokine interleukin-8 (CXCL8) is a key mediator of inflammation in airways of patients with cystic fibrosis (CF). Glycosaminoglycans (GAGs) possess the ability to influence the chemokine profile of the CF lung by binding CXCL8 and protecting it from proteolytic degradation. CXCL8 is maintained in an active state by this glycan interaction thus increasing infiltration of immune cells such as neutrophils into the lungs. As the CXCL8-based decoy PA401 displays no chemotactic activity, yet demonstrates glycan binding affinity, the aim of this study was to investigate the anti-inflammatory effect of PA401 on CXCL8 levels, and activity, in CF airway samples in vitro. METHODS: Bronchoalveolar lavage fluid (BALF) was collected from patients with CF homozygous for the ΔF508 mutation (n=13). CXCL8 in CF BALF pre and post exposure to PA401 was quantified by ELISA. Western blot analysis was used to determine PA401 degradation in CF BALF. The ex vivo chemotactic activity of purified neutrophils in response to CF airway secretions was evaluated post exposure to PA401 by use of a Boyden chamber-based motility assay. RESULTS: Exposure of CF BALF to increasing concentrations of PA401 (50-1000pg/ml) over a time course of 2-12h in vitro, significantly reduced the level of detectable CXCL8 (P CONCLUSION: PA401 can disrupt CXCL8:GAG complexes, rendering the chemokine susceptible to proteolytic degradation. Clinical application of a CXCL8 decoy, such as PA401, may serve to decrease the inflammatory burden in the CF lung in vivo

Soluble syndecan-3 binds chemokines, reduces leukocyte migration in vitro and ameliorates disease severity in models of rheumatoid arthritis

Background Syndecans are heparan sulfate proteoglycans that occur in membrane-bound or soluble forms. Syndecan-3, the least well-characterised of the syndecan family, is highly expressed on synovial endothelial cells in rheumatoid arthritis patients. Here, it binds pro-inflammatory chemokines with evidence for a role in chemokine presentation and leukocyte trafficking into the joint, promoting the inflammatory response. In this study, we explored the role of soluble syndecan-3 as a binder of chemokines and as an anti-inflammatory and therapeutic molecule. Methods A human monocytic cell line and CD14+ PBMCs were utilised in both Boyden chamber and trans-endothelial migration assays. Soluble syndecan-3 was tested in antigen-induced and collagen-induced in vivo arthritis models in mice. ELISA and isothermal fluorescence titration assays assessed the binding affinities. Syndecan-3 expression was identified by flow cytometry and PCR, and levels of shedding by ELISA. Results Using in vitro and in vivo models, soluble syndecan-3 inhibited leukocyte migration in vitro in response to CCL7 and its administration in murine models of rheumatoid arthritis reduced histological disease severity. Using isothermal fluorescence titration, the binding affinity of soluble syndecan-3 to inflammatory chemokines CCL2, CCL7 and CXCL8 was determined, revealing little difference, with Kds in the low nM range. TNFα increased cell surface expression and shedding of syndecan-3 from cultured human endothelial cells. Furthermore, soluble syndecan-3 occurred naturally in the sera of patients with rheumatoid arthritis and periodontitis, and its levels correlated with syndecan-1. Conclusions This study shows that the addition of soluble syndecan-3 may represent an alternative therapeutic approach in inflammatory disease